Overview

Dataset statistics

Number of variables10
Number of observations4829
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory160.5 KiB
Average record size in memory34.0 B

Variable types

Numeric9
Categorical1

Warnings

msno has a high cardinality: 4760 distinct values High cardinality
num_25 is highly correlated with num_unqHigh correlation
num_50 is highly correlated with num_75High correlation
num_75 is highly correlated with num_50High correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_25 and 2 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
num_25 is highly correlated with num_50 and 1 other fieldsHigh correlation
num_50 is highly correlated with num_25High correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_25 and 2 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
num_100 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_100 and 1 other fieldsHigh correlation
total_secs is highly correlated with num_100 and 1 other fieldsHigh correlation
num_100 is highly correlated with num_unqHigh correlation
num_25 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_unq is highly correlated with num_100 and 3 other fieldsHigh correlation
num_75 is highly correlated with num_unq and 1 other fieldsHigh correlation
num_50 is highly correlated with num_25 and 2 other fieldsHigh correlation
msno is uniformly distributed Uniform
df_index has unique values Unique
num_25 has 1215 (25.2%) zeros Zeros
num_50 has 2283 (47.3%) zeros Zeros
num_75 has 2595 (53.7%) zeros Zeros
num_985 has 2537 (52.5%) zeros Zeros
num_100 has 167 (3.5%) zeros Zeros

Reproduction

Analysis started2023-05-18 18:15:46.395372
Analysis finished2023-05-18 18:15:57.186335
Duration10.79 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct4829
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2391687.621
Minimum307
Maximum4826962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.9 KiB
2023-05-18T18:15:57.303715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum307
5-th percentile241996
Q11187990
median2389166
Q33577630
95-th percentile4573631.2
Maximum4826962
Range4826655
Interquartile range (IQR)2389640

Descriptive statistics

Standard deviation1388278.19
Coefficient of variation (CV)0.5804596629
Kurtosis-1.201134541
Mean2391687.621
Median Absolute Deviation (MAD)1195287
Skewness0.01530554185
Sum1.154945952 × 1010
Variance1.927316333 × 1012
MonotonicityNot monotonic
2023-05-18T18:15:57.450911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24157801
 
< 0.1%
42645481
 
< 0.1%
31164611
 
< 0.1%
1861331
 
< 0.1%
11949201
 
< 0.1%
39227861
 
< 0.1%
34959571
 
< 0.1%
33936301
 
< 0.1%
20074541
 
< 0.1%
35980391
 
< 0.1%
Other values (4819)4819
99.8%
ValueCountFrequency (%)
3071
< 0.1%
3941
< 0.1%
8741
< 0.1%
11081
< 0.1%
22791
< 0.1%
23091
< 0.1%
24811
< 0.1%
27911
< 0.1%
36261
< 0.1%
43051
< 0.1%
ValueCountFrequency (%)
48269621
< 0.1%
48263251
< 0.1%
48257971
< 0.1%
48256551
< 0.1%
48254111
< 0.1%
48250171
< 0.1%
48246601
< 0.1%
48234561
< 0.1%
48232151
< 0.1%
48208491
< 0.1%

msno
Categorical

HIGH CARDINALITY
UNIFORM

Distinct4760
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size37.9 KiB
qa7Tqz37oD5iGZHBDazEyZOSxanjpfoJwU6sM4tlM3c=
 
2
elxZ+SjElCV79jtd1tiZGJVRc+FcqtfU2NnxGhmrgn0=
 
2
mpQI3OVbrSTDBE5+7oQ/uGmjgJfalefAkkfpjIjqTqw=
 
2
B3/t5W3l4DGgqZvSDNNqj74PEkcSMUdeD0HzARUXVF8=
 
2
Uqm5yx0ZfBNVbpHehhHZt2tQ9zDuwmpxnVlbar/YkEk=
 
2
Other values (4755)
4819 

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters212476
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4691 ?
Unique (%)97.1%

Sample

1st rowtej+Qj4xcYOl/Eo/m2s7tOajEsQaaKdOgkInffLDF1E=
2nd rowpYFqXAy0HELNV4ZhfcrzOxeb81Oh8dLx+5hRaA9rV14=
3rd rowfi4aWbGcpaowRmm9yBoZUkWoYveFvVu2TCU+3bWWvtk=
4th rowsQ4z126F7JuRtOuDKhBBhKcaEw9jSrU5Tkal/uZhtfA=
5th rowXCbodeB083/UlSDlanPQbWQDdNNLUdyOjbIJz1KW5/Q=

Common Values

ValueCountFrequency (%)
qa7Tqz37oD5iGZHBDazEyZOSxanjpfoJwU6sM4tlM3c=2
 
< 0.1%
elxZ+SjElCV79jtd1tiZGJVRc+FcqtfU2NnxGhmrgn0=2
 
< 0.1%
mpQI3OVbrSTDBE5+7oQ/uGmjgJfalefAkkfpjIjqTqw=2
 
< 0.1%
B3/t5W3l4DGgqZvSDNNqj74PEkcSMUdeD0HzARUXVF8=2
 
< 0.1%
Uqm5yx0ZfBNVbpHehhHZt2tQ9zDuwmpxnVlbar/YkEk=2
 
< 0.1%
sVW70i8voMj/mCcxh3hhKWEMv2BvXVbkMub3WR0lGFE=2
 
< 0.1%
q+nzp/y18gZ1yEIoT5bjYYJZvqtKsF9NhQHqIePfuxw=2
 
< 0.1%
gjvctqyEfdNQ0yT9B9Glqk03VgFKqW8HrxUWl37kr/o=2
 
< 0.1%
SmO89dWA5GCbSMzD4NwjJAHjTgcpwD9JwogNPSiElu8=2
 
< 0.1%
ffyA5RppImQqtkKOzihR5L7KOnaCzB85i9TGdBehAAo=2
 
< 0.1%
Other values (4750)4809
99.6%

Length

2023-05-18T18:15:57.957209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qa7tqz37od5igzhbdazeyzosxanjpfojwu6sm4tlm3c2
 
< 0.1%
elxz+sjelcv79jtd1tizgjvrc+fcqtfu2nnxghmrgn02
 
< 0.1%
mpqi3ovbrstdbe5+7oq/ugmjgjfalefakkfpjijqtqw2
 
< 0.1%
b3/t5w3l4dggqzvsdnnqj74pekcsmuded0hzaruxvf82
 
< 0.1%
uqm5yx0zfbnvbphehhhzt2tq9zduwmpxnvlbar/ykek2
 
< 0.1%
svw70i8vomj/mccxh3hhkwemv2bvxvbkmub3wr0lgfe2
 
< 0.1%
q+nzp/y18gz1yeiot5bjyyjzvqtksf9nhqhqiepfuxw2
 
< 0.1%
gjvctqyefdnq0yt9b9glqk03vgfkqw8hrxuwl37kr/o2
 
< 0.1%
smo89dwa5gcbsmzd4nwjjahjtgcpwd9jwognpsielu82
 
< 0.1%
ffya5rppimqqtkkozihr5l7konaczb85i9tgdbehaao2
 
< 0.1%
Other values (4750)4809
99.6%

Most occurring characters

ValueCountFrequency (%)
=4829
 
2.3%
03569
 
1.7%
Q3528
 
1.7%
U3514
 
1.7%
I3506
 
1.7%
g3500
 
1.6%
w3488
 
1.6%
o3487
 
1.6%
A3479
 
1.6%
E3469
 
1.6%
Other values (55)176107
82.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter84609
39.8%
Lowercase Letter84056
39.6%
Decimal Number32707
 
15.4%
Math Symbol7987
 
3.8%
Other Punctuation3117
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g3500
 
4.2%
w3488
 
4.1%
o3487
 
4.1%
k3450
 
4.1%
c3414
 
4.1%
s3361
 
4.0%
h3239
 
3.9%
z3235
 
3.8%
y3233
 
3.8%
t3220
 
3.8%
Other values (16)50429
60.0%
Uppercase Letter
ValueCountFrequency (%)
Q3528
 
4.2%
U3514
 
4.2%
I3506
 
4.1%
A3479
 
4.1%
E3469
 
4.1%
Y3463
 
4.1%
M3443
 
4.1%
B3249
 
3.8%
V3242
 
3.8%
R3230
 
3.8%
Other values (16)50486
59.7%
Decimal Number
ValueCountFrequency (%)
03569
10.9%
43414
10.4%
83384
10.3%
13256
10.0%
23211
9.8%
53185
9.7%
73182
9.7%
33176
9.7%
63176
9.7%
93154
9.6%
Math Symbol
ValueCountFrequency (%)
=4829
60.5%
+3158
39.5%
Other Punctuation
ValueCountFrequency (%)
/3117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin168665
79.4%
Common43811
 
20.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q3528
 
2.1%
U3514
 
2.1%
I3506
 
2.1%
g3500
 
2.1%
w3488
 
2.1%
o3487
 
2.1%
A3479
 
2.1%
E3469
 
2.1%
Y3463
 
2.1%
k3450
 
2.0%
Other values (42)133781
79.3%
Common
ValueCountFrequency (%)
=4829
11.0%
03569
 
8.1%
43414
 
7.8%
83384
 
7.7%
13256
 
7.4%
23211
 
7.3%
53185
 
7.3%
73182
 
7.3%
33176
 
7.2%
63176
 
7.2%
Other values (3)9429
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII212476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
=4829
 
2.3%
03569
 
1.7%
Q3528
 
1.7%
U3514
 
1.7%
I3506
 
1.7%
g3500
 
1.6%
w3488
 
1.6%
o3487
 
1.6%
A3479
 
1.6%
E3469
 
1.6%
Other values (55)176107
82.9%

date
Real number (ℝ≥0)

Distinct678
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20160880.03
Minimum20150116
Maximum20170228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-05-18T18:15:58.080662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20150116
5-th percentile20150830
Q120160321
median20160810
Q320161127
95-th percentile20170211
Maximum20170228
Range20112
Interquartile range (IQR)806

Descriptive statistics

Standard deviation5349.685205
Coefficient of variation (CV)0.0002653497862
Kurtosis0.2369621034
Mean20160880.03
Median Absolute Deviation (MAD)400
Skewness-0.07126137547
Sum9.735688966 × 1010
Variance28619131.79
MonotonicityNot monotonic
2023-05-18T18:15:58.228304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017011121
 
0.4%
2016121219
 
0.4%
2016103119
 
0.4%
2016082619
 
0.4%
2017021619
 
0.4%
2017020318
 
0.4%
2016122218
 
0.4%
2017010318
 
0.4%
2017022218
 
0.4%
2017012518
 
0.4%
Other values (668)4642
96.1%
ValueCountFrequency (%)
201501161
< 0.1%
201501261
< 0.1%
201502081
< 0.1%
201502101
< 0.1%
201502131
< 0.1%
201502151
< 0.1%
201502181
< 0.1%
201502251
< 0.1%
201502282
< 0.1%
201503021
< 0.1%
ValueCountFrequency (%)
2017022814
0.3%
2017022714
0.3%
2017022610
0.2%
2017022514
0.3%
2017022416
0.3%
2017022315
0.3%
2017022218
0.4%
2017022114
0.3%
2017022012
0.2%
2017021912
0.2%

num_25
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct96
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.804721474
Minimum0
Maximum292
Zeros1215
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:15:58.383452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile28.6
Maximum292
Range292
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.48338795
Coefficient of variation (CV)1.981475363
Kurtosis78.42058902
Mean6.804721474
Median Absolute Deviation (MAD)2
Skewness6.505306717
Sum32860
Variance181.8017507
MonotonicityNot monotonic
2023-05-18T18:15:58.518140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01215
25.2%
1748
15.5%
2492
10.2%
3355
 
7.4%
4280
 
5.8%
5222
 
4.6%
6176
 
3.6%
7145
 
3.0%
8126
 
2.6%
10102
 
2.1%
Other values (86)968
20.0%
ValueCountFrequency (%)
01215
25.2%
1748
15.5%
2492
10.2%
3355
 
7.4%
4280
 
5.8%
5222
 
4.6%
6176
 
3.6%
7145
 
3.0%
8126
 
2.6%
998
 
2.0%
ValueCountFrequency (%)
2921
< 0.1%
2041
< 0.1%
1901
< 0.1%
1831
< 0.1%
1451
< 0.1%
1361
< 0.1%
1211
< 0.1%
1171
< 0.1%
1061
< 0.1%
1051
< 0.1%

num_50
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.597018016
Minimum0
Maximum113
Zeros2283
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:15:58.654631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum113
Range113
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.852978411
Coefficient of variation (CV)2.412607981
Kurtosis275.2539392
Mean1.597018016
Median Absolute Deviation (MAD)1
Skewness12.38751595
Sum7712
Variance14.84544264
MonotonicityNot monotonic
2023-05-18T18:15:58.778659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
02283
47.3%
11098
22.7%
2578
 
12.0%
3317
 
6.6%
4158
 
3.3%
5110
 
2.3%
669
 
1.4%
737
 
0.8%
931
 
0.6%
831
 
0.6%
Other values (27)117
 
2.4%
ValueCountFrequency (%)
02283
47.3%
11098
22.7%
2578
 
12.0%
3317
 
6.6%
4158
 
3.3%
5110
 
2.3%
669
 
1.4%
737
 
0.8%
831
 
0.6%
931
 
0.6%
ValueCountFrequency (%)
1131
 
< 0.1%
1031
 
< 0.1%
631
 
< 0.1%
461
 
< 0.1%
441
 
< 0.1%
401
 
< 0.1%
381
 
< 0.1%
361
 
< 0.1%
323
0.1%
301
 
< 0.1%

num_75
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9803271899
Minimum0
Maximum43
Zeros2595
Zeros (%)53.7%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:15:58.900667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum43
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.824822578
Coefficient of variation (CV)1.86144238
Kurtosis98.06264141
Mean0.9803271899
Median Absolute Deviation (MAD)0
Skewness6.717025463
Sum4734
Variance3.329977441
MonotonicityNot monotonic
2023-05-18T18:15:59.007127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
02595
53.7%
11178
24.4%
2499
 
10.3%
3261
 
5.4%
4123
 
2.5%
565
 
1.3%
641
 
0.8%
820
 
0.4%
719
 
0.4%
95
 
0.1%
Other values (14)23
 
0.5%
ValueCountFrequency (%)
02595
53.7%
11178
24.4%
2499
 
10.3%
3261
 
5.4%
4123
 
2.5%
565
 
1.3%
641
 
0.8%
719
 
0.4%
820
 
0.4%
95
 
0.1%
ValueCountFrequency (%)
431
< 0.1%
331
< 0.1%
231
< 0.1%
221
< 0.1%
211
< 0.1%
191
< 0.1%
181
< 0.1%
171
< 0.1%
151
< 0.1%
141
< 0.1%

num_985
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.08987368
Minimum0
Maximum75
Zeros2537
Zeros (%)52.5%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:15:59.119886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum75
Range75
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.50637112
Coefficient of variation (CV)2.299689557
Kurtosis279.9932589
Mean1.08987368
Median Absolute Deviation (MAD)0
Skewness12.65345601
Sum5263
Variance6.281896194
MonotonicityNot monotonic
2023-05-18T18:15:59.235157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
02537
52.5%
11220
25.3%
2460
 
9.5%
3244
 
5.1%
4151
 
3.1%
589
 
1.8%
644
 
0.9%
723
 
0.5%
811
 
0.2%
1110
 
0.2%
Other values (17)40
 
0.8%
ValueCountFrequency (%)
02537
52.5%
11220
25.3%
2460
 
9.5%
3244
 
5.1%
4151
 
3.1%
589
 
1.8%
644
 
0.9%
723
 
0.5%
811
 
0.2%
99
 
0.2%
ValueCountFrequency (%)
751
< 0.1%
611
< 0.1%
471
< 0.1%
421
< 0.1%
371
< 0.1%
301
< 0.1%
261
< 0.1%
251
< 0.1%
181
< 0.1%
171
< 0.1%

num_100
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct201
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.30979499
Minimum0
Maximum375
Zeros167
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:15:59.373426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median16
Q335
95-th percentile101
Maximum375
Range375
Interquartile range (IQR)29

Descriptive statistics

Standard deviation35.71394146
Coefficient of variation (CV)1.261540095
Kurtosis11.79313726
Mean28.30979499
Median Absolute Deviation (MAD)12
Skewness2.813764771
Sum136708
Variance1275.485614
MonotonicityNot monotonic
2023-05-18T18:15:59.512578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1231
 
4.8%
5194
 
4.0%
4176
 
3.6%
3172
 
3.6%
8168
 
3.5%
0167
 
3.5%
2159
 
3.3%
7148
 
3.1%
6145
 
3.0%
11131
 
2.7%
Other values (191)3138
65.0%
ValueCountFrequency (%)
0167
3.5%
1231
4.8%
2159
3.3%
3172
3.6%
4176
3.6%
5194
4.0%
6145
3.0%
7148
3.1%
8168
3.5%
9124
2.6%
ValueCountFrequency (%)
3751
< 0.1%
3731
< 0.1%
3131
< 0.1%
2901
< 0.1%
2801
< 0.1%
2792
< 0.1%
2661
< 0.1%
2431
< 0.1%
2421
< 0.1%
2412
< 0.1%

num_unq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct176
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.53365086
Minimum1
Maximum299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:15:59.660506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median19
Q339
95-th percentile90
Maximum299
Range298
Interquartile range (IQR)31

Descriptive statistics

Standard deviation30.28583036
Coefficient of variation (CV)1.061407477
Kurtosis8.142531258
Mean28.53365086
Median Absolute Deviation (MAD)13
Skewness2.306616814
Sum137789
Variance917.2315207
MonotonicityNot monotonic
2023-05-18T18:15:59.796196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200
 
4.1%
3183
 
3.8%
5165
 
3.4%
2160
 
3.3%
8152
 
3.1%
4151
 
3.1%
7145
 
3.0%
9140
 
2.9%
6124
 
2.6%
11121
 
2.5%
Other values (166)3288
68.1%
ValueCountFrequency (%)
1200
4.1%
2160
3.3%
3183
3.8%
4151
3.1%
5165
3.4%
6124
2.6%
7145
3.0%
8152
3.1%
9140
2.9%
10116
2.4%
ValueCountFrequency (%)
2991
< 0.1%
2841
< 0.1%
2811
< 0.1%
2421
< 0.1%
2181
< 0.1%
2071
< 0.1%
1961
< 0.1%
1951
< 0.1%
1941
< 0.1%
1911
< 0.1%

total_secs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3383
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite3
Infinite (%)0.1%
Meaninf
Minimum0.7431640625
Maximuminf
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-05-18T18:15:59.939718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.7431640625
5-th percentile342.4
Q11881
median4496
Q39432
95-th percentile26019.2
Maximuminf
Rangeinf
Interquartile range (IQR)7551

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)3132
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2023-05-18T18:16:00.094869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82886
 
0.1%
55085
 
0.1%
44965
 
0.1%
27105
 
0.1%
21225
 
0.1%
83445
 
0.1%
89205
 
0.1%
46725
 
0.1%
88805
 
0.1%
47725
 
0.1%
Other values (3373)4778
98.9%
ValueCountFrequency (%)
0.74316406251
< 0.1%
0.83593751
< 0.1%
1.8388671881
< 0.1%
2.1816406251
< 0.1%
2.667968751
< 0.1%
3.81251
< 0.1%
4.410156251
< 0.1%
4.6251
< 0.1%
6.281251
< 0.1%
7.628906251
< 0.1%
ValueCountFrequency (%)
inf3
0.1%
646401
 
< 0.1%
630401
 
< 0.1%
627201
 
< 0.1%
620481
 
< 0.1%
609281
 
< 0.1%
608001
 
< 0.1%
601281
 
< 0.1%
590081
 
< 0.1%
580481
 
< 0.1%

Interactions

2023-05-18T18:15:47.091452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:47.241275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:47.395557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:47.508293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:47.619095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:47.726631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:47.841023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:47.955496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:48.067854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:48.183269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:48.310339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:48.445863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:48.572642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:48.697443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:48.819977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:48.949457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:49.079850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:49.207576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:49.336856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:49.450068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:49.574187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:49.689599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:49.802777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:49.912457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:50.029244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:50.145837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:50.260936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:50.377041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:50.488256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:50.608646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:50.719575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:50.828587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:50.935597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:51.049205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:51.163173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:51.274836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:51.388052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:51.494827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:51.612809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:51.722391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:51.828603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:51.931767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:52.041384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:52.151964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:52.259778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:52.620950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:52.738343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:52.866125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:52.983906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:53.099004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:53.213946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:53.333638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:53.454229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:53.571818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:53.691858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:53.809328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:53.936779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:54.054076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:54.170005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:54.283024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:54.402672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:54.522412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:54.641441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:54.756884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:54.869513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:54.992843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:55.106954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:55.218644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:55.328527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:55.445198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:55.563561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:55.677973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:55.794653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:55.909379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:56.033576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:56.148454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:56.261122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:56.371661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:56.489939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:56.606209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-18T18:15:56.721881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-18T18:16:00.213574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-05-18T18:16:00.362041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-05-18T18:16:00.509239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-05-18T18:16:00.661083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-05-18T18:15:56.911160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-18T18:15:57.108816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexmsnodatenum_25num_50num_75num_985num_100num_unqtotal_secs
02415780tej+Qj4xcYOl/Eo/m2s7tOajEsQaaKdOgkInffLDF1E=20170129110132178368.0000
14584805pYFqXAy0HELNV4ZhfcrzOxeb81Oh8dLx+5hRaA9rV14=201702124200561396.0000
21725913fi4aWbGcpaowRmm9yBoZUkWoYveFvVu2TCU+3bWWvtk=20170206000010102768.0000
33616910sQ4z126F7JuRtOuDKhBBhKcaEw9jSrU5Tkal/uZhtfA=201612230010561215.0000
41068647XCbodeB083/UlSDlanPQbWQDdNNLUdyOjbIJz1KW5/Q=2016053010000114.1875
52120337HEy4RCCczLtnKgTNc1e8cqF89rHzfaz0l+YnQk+VqZg=20160823001012403.0000
61516916dM3SfMSYNrASG21pjaRKCDLybShNM3UWrQ6B6qQy0xA=20160403000042429840.0000
72636443v4td4+kAOP1ZQ65i23wR98oCC/683N9ckiUG/wXz1zg=20160522000044802.0000
81360093A5g2OKmuW+tNykWvB3VGLqqg3L/Q51w5EkKTvHl6P1s=20160820700114194168.0000
941541102b7ZDK/hU1mPFSJ49g0hrnJfiIb4dKpHAmnbNM2D9XY=201612020010451161.0000

Last rows

df_indexmsnodatenum_25num_50num_75num_985num_100num_unqtotal_secs
481946022473Qvzm/ImdX+QpPhA5PEdxanffJ5gClVLLGYQt3NYfVY=20160623401122285936.0
48203139202fk/UAHsgoKFbsUZ5zaWQwqqq+LDY2frHi+PC8sA+msg=20161228300011122836.0
48211459242nlng/ax+vA/dN9usZiUIOfTnfi4pkeG58DUPCsOiVJ0=20151222120001664942112.0
48223647998jYSQWJvS3O+AhzKMxE1UVBUBQiSRw4yLiyAqce2cy/8=2016111241139183052.0
48232071228/EJJnh5LlU677MYh/XUCrWwhyBLFTBCpdWWPmtrjmdc=20160302000029247176.0
48242736932MlrUpeXRX5kzSDrxie2Ie6imxsz4Uyq6xNU19Mv28ZU=20160315000123777.5
48252863801mt9qBgkjws2VzYX3PHiGw3VrT/d8ut/LlvPzVdwYz0w=201604112313110361917.0
48261701042YCzjkI8TLKPe8L/6s8lAlpqffOrGzFWkMsiYdjtgU8A=20160315000023225532.0
48272098588pEOXyeH30m9Gg5pzMuPKdJzJv8z7lkg2SJ7d4+9rHcI=2016121835115868721904.0
482825150986eLykvU/uxBfjHpRpYstV++XmVbI7NDWpgVbTCBIqQM=20161212511121175876.0